import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')

calendar = pd.read_csv('calendar.csv')
dictionary = pd.read_csv('data_dictionary.csv')
inventory = pd.read_csv('inventory.csv')
products = pd.read_csv('products.csv')
sales = pd.read_csv('sales.csv')
stores = pd.read_csv('stores.csv')

dictionary

sales.head()

products.head()

stores.head()

inventory.head()

calendar

sales_products = pd.merge(sales , products , how = 'inner' , on = 'Product_ID')

sales_products.head()

sales_products_stores = pd.merge(sales_products , stores , how = 'inner' , on = 'Store_ID')

sales_products_stores.head()

sales_products_stores_inventory = pd.merge(sales_products_stores , inventory , how = 'inner' , on = ['Product_ID' , 'Store_ID'])

sales_products_stores_inventory.head()

df = sales_products_stores_inventory

df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 823030 entries, 0 to 823029
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   Sale_ID           823030 non-null  int64 
 1   Date              823030 non-null  object
 2   Store_ID          823030 non-null  int64 
 3   Product_ID        823030 non-null  int64 
 4   Units             823030 non-null  int64 
 5   Product_Name      823030 non-null  object
 6   Product_Category  823030 non-null  object
 7   Product_Cost      823030 non-null  object
 8   Product_Price     823030 non-null  object
 9   Store_Name        823030 non-null  object
 10  Store_City        823030 non-null  object
 11  Store_Location    823030 non-null  object
 12  Store_Open_Date   823030 non-null  object
 13  Stock_On_Hand     823030 non-null  int64 
dtypes: int64(5), object(9)
memory usage: 87.9+ MB

# Numerical (int64,float64): Sale_ID, Store_ID, Product_ID, Units, Stock_On_Hand
# Categorical/Object : Date, Product_Name, Product_Category, Product_Cost, Product_Price, Store_Name, Store_City, Store_Location, Store_Open_Date

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 823030 entries, 0 to 823029
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   Sale_ID           823030 non-null  int64 
 1   Date              823030 non-null  object
 2   Store_ID          823030 non-null  int64 
 3   Product_ID        823030 non-null  int64 
 4   Units             823030 non-null  int64 
 5   Product_Name      823030 non-null  object
 6   Product_Category  823030 non-null  object
 7   Product_Cost      823030 non-null  object
 8   Product_Price     823030 non-null  object
 9   Store_Name        823030 non-null  object
 10  Store_City        823030 non-null  object
 11  Store_Location    823030 non-null  object
 12  Store_Open_Date   823030 non-null  object
 13  Stock_On_Hand     823030 non-null  int64 
dtypes: int64(5), object(9)
memory usage: 87.9+ MB

df['Date'] = pd.to_datetime(df['Date'])
df['Store_Open_Date'] = pd.to_datetime(df['Store_Open_Date'])

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 823030 entries, 0 to 823029
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Sale_ID           823030 non-null  int64         
 1   Date              823030 non-null  datetime64[ns]
 2   Store_ID          823030 non-null  int64         
 3   Product_ID        823030 non-null  int64         
 4   Units             823030 non-null  int64         
 5   Product_Name      823030 non-null  object        
 6   Product_Category  823030 non-null  object        
 7   Product_Cost      823030 non-null  object        
 8   Product_Price     823030 non-null  object        
 9   Store_Name        823030 non-null  object        
 10  Store_City        823030 non-null  object        
 11  Store_Location    823030 non-null  object        
 12  Store_Open_Date   823030 non-null  datetime64[ns]
 13  Stock_On_Hand     823030 non-null  int64         
dtypes: datetime64[ns](2), int64(5), object(7)
memory usage: 87.9+ MB

df['Product_Cost'] = df['Product_Cost'].str.replace('$' , '')
df['Product_Price'] = df['Product_Price'].str.replace('$' , '')

print(df['Product_Cost'].head())
print(df['Product_Price'].head())

0     9.99 
1     9.99 
2     3.99 
3    11.99 
4    34.99 
Name: Product_Cost, dtype: object
0    12.99 
1    15.99 
2     6.99 
3    15.99 
4    39.99 
Name: Product_Price, dtype: object

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 823030 entries, 0 to 823029
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Sale_ID           823030 non-null  int64         
 1   Date              823030 non-null  datetime64[ns]
 2   Store_ID          823030 non-null  int64         
 3   Product_ID        823030 non-null  int64         
 4   Units             823030 non-null  int64         
 5   Product_Name      823030 non-null  object        
 6   Product_Category  823030 non-null  object        
 7   Product_Cost      823030 non-null  object        
 8   Product_Price     823030 non-null  object        
 9   Store_Name        823030 non-null  object        
 10  Store_City        823030 non-null  object        
 11  Store_Location    823030 non-null  object        
 12  Store_Open_Date   823030 non-null  datetime64[ns]
 13  Stock_On_Hand     823030 non-null  int64         
dtypes: datetime64[ns](2), int64(5), object(7)
memory usage: 87.9+ MB

df['Product_Cost'] = df['Product_Cost'].astype('float64')
df['Product_Price'] = df['Product_Price'].astype('float64')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 823030 entries, 0 to 823029
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Sale_ID           823030 non-null  int64         
 1   Date              823030 non-null  datetime64[ns]
 2   Store_ID          823030 non-null  int64         
 3   Product_ID        823030 non-null  int64         
 4   Units             823030 non-null  int64         
 5   Product_Name      823030 non-null  object        
 6   Product_Category  823030 non-null  object        
 7   Product_Cost      823030 non-null  float64       
 8   Product_Price     823030 non-null  float64       
 9   Store_Name        823030 non-null  object        
 10  Store_City        823030 non-null  object        
 11  Store_Location    823030 non-null  object        
 12  Store_Open_Date   823030 non-null  datetime64[ns]
 13  Stock_On_Hand     823030 non-null  int64         
dtypes: datetime64[ns](2), float64(2), int64(5), object(5)
memory usage: 87.9+ MB

df['Revenue'] = df['Units'] * df['Product_Price']
df['Profit'] = df['Units'] * (df['Product_Price'] - df['Product_Cost'])

df.head()

# Numerical Variables to be analyzed : Units, Stock_On_Hand, Product_Cost, Product_Price, derived Revenue, Profit

# 1. Central tendency (mean, median, mode) of Units sold per transaction
print("\n1. Central Tendency of 'Units' sold per transaction:")
print(f"   Mean Units: {df['Units'].mean():.2f}")
print(f"   Median Units: {df['Units'].median():.2f}")
print(f"   Mode Units: {df['Units'].mode().tolist()}") # .mode() can return multiple if equally frequent

print('='*100)

# 2. Typical Stock_On_Hand, Minimum and Maximum Stock Levels
print("\n2. Statistics for 'Stock_On_Hand':")
print(f"   Mean Stock On Hand: {df['Stock_On_Hand'].mean():.2f}")
print(f"   Median Stock On Hand: {df['Stock_On_Hand'].median():.2f}")
print(f"   Minimum Stock On Hand: {df['Stock_On_Hand'].min():.2f}")
print(f"   Maximum Stock On Hand: {df['Stock_On_Hand'].max():.2f}")

print('='*100)

# 3. Distribution of Product_Cost and Product_Price (and hints for outliers)
print("\n3. Distribution of 'Product_Cost' and 'Product_Price':")
# .describe() provides count, mean, std, min, 25%, 50%, 75%, max
# Outliers can be inferred by comparing min/max to the quartiles (25%, 75%)
# or by observing a large standard deviation relative to the mean.
print("\n   Product_Cost Description:")
print(df['Product_Cost'].describe())
print("\n   Product_Price Description:")
print(df['Product_Price'].describe())

print('='*100)

# 4. Total and average Revenue and Profit per transaction
print("\n4. Total and Average 'Revenue' and 'Profit' per transaction:")
print(f"   Total Revenue: {df['Revenue'].sum():,.2f}")
print(f"   Average Revenue per Transaction: {df['Revenue'].mean():,.2f}")
print(f"   Total Profit: {df['Profit'].sum():,.2f}")
print(f"   Average Profit per Transaction: {df['Profit'].mean():,.2f}")

print('='*100)

# 5. Skewness and kurtosis of Units, Revenue, and Profit
print("\n5. Skewness and Kurtosis of 'Units', 'Revenue', and 'Profit':")
print(f"   Skewness of Units: {df['Units'].skew():.4f}")
print(f"   Kurtosis of Units: {df['Units'].kurtosis():.4f}")
print(f"   Skewness of Revenue: {df['Revenue'].skew():.4f}")
print(f"   Kurtosis of Revenue: {df['Revenue'].kurtosis():.4f}")
print(f"   Skewness of Profit: {df['Profit'].skew():.4f}")
print(f"   Kurtosis of Profit: {df['Profit'].kurtosis():.4f}")

1. Central Tendency of 'Units' sold per transaction:
   Mean Units: 1.32
   Median Units: 1.00
   Mode Units: [1]
====================================================================================================

2. Statistics for 'Stock_On_Hand':
   Mean Stock On Hand: 25.32
   Median Stock On Hand: 18.00
   Minimum Stock On Hand: 0.00
   Maximum Stock On Hand: 139.00
====================================================================================================

3. Distribution of 'Product_Cost' and 'Product_Price':

   Product_Cost Description:
count    823030.000000
mean         10.026130
std           7.824972
min           1.990000
25%           3.990000
50%           7.990000
75%          13.990000
max          34.990000
Name: Product_Cost, dtype: float64

   Product_Price Description:
count    823030.000000
mean         13.797757
std           8.690296
min           2.990000
25%           6.990000
50%          12.990000
75%          15.990000
max          39.990000
Name: Product_Price, dtype: float64
====================================================================================================

4. Total and Average 'Revenue' and 'Profit' per transaction:
   Total Revenue: 14,375,257.09
   Average Revenue per Transaction: 17.47
   Total Profit: 3,967,329.00
   Average Profit per Transaction: 4.82
====================================================================================================

5. Skewness and Kurtosis of 'Units', 'Revenue', and 'Profit':
   Skewness of Units: 4.4901
   Kurtosis of Units: 47.8224
   Skewness of Revenue: 5.6874
   Kurtosis of Revenue: 120.5313
   Skewness of Profit: 4.2468
   Kurtosis of Profit: 46.8556

df['Product_Price'].value_counts()

df['Product_Cost'].value_counts()

# --- Define the numerical columns to plot ---

columns_to_plot = ['Units', 'Stock_On_Hand', 'Product_Cost', 'Product_Price', 'Revenue', 'Profit']

# Set a professional plotting style
# sns.set_theme(style="whitegrid")

# Create a figure and a grid of subplots (2 rows, 3 columns)
fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 10)) # Adjust figsize as needed
axes = axes.flatten() # Flatten the 2x3 array of axes for easy iteration

fig.suptitle('Distribution of Key Numerical Variables (Histograms with KDE)', fontsize=20, y=1.02) # Main title for the entire figure

# Loop through each column and plot its histogram with KDE
for i, col in enumerate(columns_to_plot):
    # Use sns.histplot for histogram and KDE (Kernel Density Estimate)
    sns.histplot(data=df, x=col, kde=True, ax=axes[i], bins=50, color='magenta', palette='Set1' , edgecolor='black')

    # Set title and labels for each subplot
    axes[i].set_title(f'Distribution of {col}', fontsize=14)
    axes[i].set_xlabel(col, fontsize=12)
    axes[i].set_ylabel('Frequency', fontsize=12)

    # Optional: Add grid for better readability
    axes[i].grid(True, linestyle='--', alpha=0.6)

# Adjust layout to prevent titles/labels from overlapping
plt.tight_layout(rect=[0, 0.03, 1, 0.98]) # [left, bottom, right, top] for padding

# Display the plot
plt.show()

# Visualizing the product price distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Price'].value_counts() , palette = 'pastel' , edgecolor = 'black' , order = df['Product_Price'].value_counts().index)

plt.title('Product price Distribution')
plt.xlabel('Product price')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the product cost distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Cost'].value_counts() , palette = 'pastel' , edgecolor = 'black' , order = df['Product_Cost'].value_counts().index)

plt.title('Product cost Distribution')
plt.xlabel('Product cost')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Categorical Variables (Product_Name, Product_Category, Store_Name, Store_City, Store_Location)

print("--- Univariate Analysis of Categorical Columns ---")

# 1. Most frequent Product_Names and Product_Categorys (count/percentage)

print("\n1. Frequency and Percentage of 'Product_Name':")
print(df['Product_Name'].value_counts())
print("\nPercentage of 'Product_Name':")
print(df['Product_Name'].value_counts(normalize=True) * 100) # Multiply by 100 for percentage

print("\n" + "="*50)

print("\nFrequency and Percentage of 'Product_Category':")
print(df['Product_Category'].value_counts())
print("\nPercentage of 'Product_Category':")
print(df['Product_Category'].value_counts(normalize=True) * 100)

# 2. Number of unique Store_Names, Store_Citys, and Store_Locations

print("\n" + "="*50)
print("\n2. Number of Unique Values for Store-related Columns:")
print(f"   Number of unique 'Store_Name's: {df['Store_Name'].nunique()}")
print(f"   Number of unique 'Store_City's: {df['Store_City'].nunique()}")
print(f"   Number of unique 'Store_Location's: {df['Store_Location'].nunique()}")

# 3. Distribution of Workclass, Education, Marital Status, etc. (counts and proportions)

print("\n" + "="*50)
print("\n3. Distribution (Counts and Proportions) of Key Categorical Columns:")

categorical_columns = [
    'Product_Name', 'Product_Category', 'Store_Name', 'Store_City', 'Store_Location'
]

for col in categorical_columns:
    print(f"\n--- Distribution for '{col}' ---")
    print("Counts:")
    print(df[col].value_counts(dropna=False)) # dropna=False to include NaN counts
    print("\nProportions (%):")
    print(df[col].value_counts(normalize=True, dropna=False) * 100)
    print("-" * 30) # Separator for clarity between columns

--- Univariate Analysis of Categorical Columns ---

1. Frequency and Percentage of 'Product_Name':
Product_Name
Colorbuds                72988
Deck Of Cards            68083
PlayDoh Can              64834
Barrel O' Slime          54078
Action Figure            48497
Lego Bricks              48030
Splash Balls             41559
Magic Sand               39293
Rubik's Cube             38703
Animal Figures           32250
Dino Egg                 28181
Dart Gun                 26203
Glass Marbles            24507
Mini Ping Pong Set       24378
Dinosaur Figures         24311
Nerf Gun                 23709
Kids Makeup Kit          21648
Hot Wheels 5-Pack        20776
PlayDoh Toolkit          20736
Gamer Headphones         15543
Etch A Sketch            11205
Toy Robot                10494
Mr. Potatohead            7804
Foam Disk Launcher        6812
Jenga                     6370
Supersoaker Water Gun     6088
PlayDoh Playset           6067
Teddy Bear                6034
Plush Pony                5278
Classic Dominoes          4146
Chutes & Ladders          3647
Monopoly                  2938
Playfoam                  2812
Uno Card Game             2543
Mini Basketball Hoop      2485
Name: count, dtype: int64

Percentage of 'Product_Name':
Product_Name
Colorbuds                8.868207
Deck Of Cards            8.272238
PlayDoh Can              7.877477
Barrel O' Slime          6.570599
Action Figure            5.892495
Lego Bricks              5.835753
Splash Balls             5.049512
Magic Sand               4.774188
Rubik's Cube             4.702502
Animal Figures           3.918448
Dino Egg                 3.424055
Dart Gun                 3.183724
Glass Marbles            2.977656
Mini Ping Pong Set       2.961982
Dinosaur Figures         2.953841
Nerf Gun                 2.880697
Kids Makeup Kit          2.630281
Hot Wheels 5-Pack        2.524331
PlayDoh Toolkit          2.519471
Gamer Headphones         1.888510
Etch A Sketch            1.361433
Toy Robot                1.275045
Mr. Potatohead           0.948204
Foam Disk Launcher       0.827673
Jenga                    0.773969
Supersoaker Water Gun    0.739706
PlayDoh Playset          0.737154
Teddy Bear               0.733145
Plush Pony               0.641289
Classic Dominoes         0.503748
Chutes & Ladders         0.443119
Monopoly                 0.356974
Playfoam                 0.341664
Uno Card Game            0.308980
Mini Basketball Hoop     0.301933
Name: proportion, dtype: float64

==================================================

Frequency and Percentage of 'Product_Category':
Product_Category
Toys                 221161
Art & Crafts         220673
Games                150937
Sports & Outdoors    131234
Electronics           99025
Name: count, dtype: int64

Percentage of 'Product_Category':
Product_Category
Toys                 26.871560
Art & Crafts         26.812267
Games                18.339186
Sports & Outdoors    15.945227
Electronics          12.031761
Name: proportion, dtype: float64

==================================================

2. Number of Unique Values for Store-related Columns:
   Number of unique 'Store_Name's: 50
   Number of unique 'Store_City's: 29
   Number of unique 'Store_Location's: 4

==================================================

3. Distribution (Counts and Proportions) of Key Categorical Columns:

--- Distribution for 'Product_Name' ---
Counts:
Product_Name
Colorbuds                72988
Deck Of Cards            68083
PlayDoh Can              64834
Barrel O' Slime          54078
Action Figure            48497
Lego Bricks              48030
Splash Balls             41559
Magic Sand               39293
Rubik's Cube             38703
Animal Figures           32250
Dino Egg                 28181
Dart Gun                 26203
Glass Marbles            24507
Mini Ping Pong Set       24378
Dinosaur Figures         24311
Nerf Gun                 23709
Kids Makeup Kit          21648
Hot Wheels 5-Pack        20776
PlayDoh Toolkit          20736
Gamer Headphones         15543
Etch A Sketch            11205
Toy Robot                10494
Mr. Potatohead            7804
Foam Disk Launcher        6812
Jenga                     6370
Supersoaker Water Gun     6088
PlayDoh Playset           6067
Teddy Bear                6034
Plush Pony                5278
Classic Dominoes          4146
Chutes & Ladders          3647
Monopoly                  2938
Playfoam                  2812
Uno Card Game             2543
Mini Basketball Hoop      2485
Name: count, dtype: int64

Proportions (%):
Product_Name
Colorbuds                8.868207
Deck Of Cards            8.272238
PlayDoh Can              7.877477
Barrel O' Slime          6.570599
Action Figure            5.892495
Lego Bricks              5.835753
Splash Balls             5.049512
Magic Sand               4.774188
Rubik's Cube             4.702502
Animal Figures           3.918448
Dino Egg                 3.424055
Dart Gun                 3.183724
Glass Marbles            2.977656
Mini Ping Pong Set       2.961982
Dinosaur Figures         2.953841
Nerf Gun                 2.880697
Kids Makeup Kit          2.630281
Hot Wheels 5-Pack        2.524331
PlayDoh Toolkit          2.519471
Gamer Headphones         1.888510
Etch A Sketch            1.361433
Toy Robot                1.275045
Mr. Potatohead           0.948204
Foam Disk Launcher       0.827673
Jenga                    0.773969
Supersoaker Water Gun    0.739706
PlayDoh Playset          0.737154
Teddy Bear               0.733145
Plush Pony               0.641289
Classic Dominoes         0.503748
Chutes & Ladders         0.443119
Monopoly                 0.356974
Playfoam                 0.341664
Uno Card Game            0.308980
Mini Basketball Hoop     0.301933
Name: proportion, dtype: float64
------------------------------

--- Distribution for 'Product_Category' ---
Counts:
Product_Category
Toys                 221161
Art & Crafts         220673
Games                150937
Sports & Outdoors    131234
Electronics           99025
Name: count, dtype: int64

Proportions (%):
Product_Category
Toys                 26.871560
Art & Crafts         26.812267
Games                18.339186
Sports & Outdoors    15.945227
Electronics          12.031761
Name: proportion, dtype: float64
------------------------------

--- Distribution for 'Store_Name' ---
Counts:
Store_Name
Maven Toys Ciudad de Mexico 2    28497
Maven Toys Ciudad de Mexico 1    24482
Maven Toys Guadalajara 3         23099
Maven Toys Toluca 1              22969
Maven Toys Monterrey 2           21300
Maven Toys Ciudad de Mexico 3    19551
Maven Toys Saltillo 1            18924
Maven Toys Guadalajara 4         18465
Maven Toys Xalapa 2              18418
Maven Toys Guanajuato 1          18139
Maven Toys Hermosillo 2          18018
Maven Toys Campeche 1            17695
Maven Toys Ciudad de Mexico 4    17439
Maven Toys Mexicali 1            16864
Maven Toys Mexicali 2            16732
Maven Toys Chihuahua 2           16580
Maven Toys Hermosillo 3          16553
Maven Toys Puebla 2              16501
Maven Toys Guanajuato 2          16494
Maven Toys Villahermosa 1        16324
Maven Toys Monterrey 3           16049
Maven Toys Guadalajara 2         16029
Maven Toys Monterrey 4           15953
Maven Toys Guadalajara 1         15926
Maven Toys Santiago 1            15873
Maven Toys Ciudad Victoria 1     15866
Maven Toys Puebla 1              15776
Maven Toys Monterrey 1           15571
Maven Toys San Luis Potosi 1     15499
Maven Toys Hermosillo 1          15202
Maven Toys Pachuca 1             14969
Maven Toys Morelia 1             14956
Maven Toys Merida 1              14875
Maven Toys Puebla 3              14802
Maven Toys Xalapa 1              14769
Maven Toys Chetumal 1            14644
Maven Toys Culiacan 1            14594
Maven Toys Aguascalientes 1      14588
Maven Toys Guanajuato 3          14569
Maven Toys Tuxtla Gutierrez 1    14441
Maven Toys Chilpancingo 1        14438
Maven Toys Saltillo 2            14166
Maven Toys Chihuahua 1           13998
Maven Toys Durango 1             13967
Maven Toys Oaxaca 1              13741
Maven Toys Cuernavaca 1          13335
Maven Toys Zacatecas 1           13264
Maven Toys La Paz 1              12924
Maven Toys Campeche 2            12805
Maven Toys Toluca 2              12397
Name: count, dtype: int64

Proportions (%):
Store_Name
Maven Toys Ciudad de Mexico 2    3.462450
Maven Toys Ciudad de Mexico 1    2.974618
Maven Toys Guadalajara 3         2.806581
Maven Toys Toluca 1              2.790785
Maven Toys Monterrey 2           2.587998
Maven Toys Ciudad de Mexico 3    2.375491
Maven Toys Saltillo 1            2.299309
Maven Toys Guadalajara 4         2.243539
Maven Toys Xalapa 2              2.237829
Maven Toys Guanajuato 1          2.203929
Maven Toys Hermosillo 2          2.189228
Maven Toys Campeche 1            2.149982
Maven Toys Ciudad de Mexico 4    2.118878
Maven Toys Mexicali 1            2.049014
Maven Toys Mexicali 2            2.032976
Maven Toys Chihuahua 2           2.014507
Maven Toys Hermosillo 3          2.011227
Maven Toys Puebla 2              2.004909
Maven Toys Guanajuato 2          2.004058
Maven Toys Villahermosa 1        1.983403
Maven Toys Monterrey 3           1.949990
Maven Toys Guadalajara 2         1.947560
Maven Toys Monterrey 4           1.938325
Maven Toys Guadalajara 1         1.935045
Maven Toys Santiago 1            1.928605
Maven Toys Ciudad Victoria 1     1.927755
Maven Toys Puebla 1              1.916820
Maven Toys Monterrey 1           1.891912
Maven Toys San Luis Potosi 1     1.883163
Maven Toys Hermosillo 1          1.847077
Maven Toys Pachuca 1             1.818767
Maven Toys Morelia 1             1.817188
Maven Toys Merida 1              1.807346
Maven Toys Puebla 3              1.798476
Maven Toys Xalapa 1              1.794467
Maven Toys Chetumal 1            1.779279
Maven Toys Culiacan 1            1.773204
Maven Toys Aguascalientes 1      1.772475
Maven Toys Guanajuato 3          1.770166
Maven Toys Tuxtla Gutierrez 1    1.754614
Maven Toys Chilpancingo 1        1.754250
Maven Toys Saltillo 2            1.721201
Maven Toys Chihuahua 1           1.700789
Maven Toys Durango 1             1.697022
Maven Toys Oaxaca 1              1.669562
Maven Toys Cuernavaca 1          1.620233
Maven Toys Zacatecas 1           1.611606
Maven Toys La Paz 1              1.570295
Maven Toys Campeche 2            1.555836
Maven Toys Toluca 2              1.506263
Name: proportion, dtype: float64
------------------------------

--- Distribution for 'Store_City' ---
Counts:
Store_City
Cuidad de Mexico    89969
Guadalajara         73519
Monterrey           68873
Hermosillo          49773
Guanajuato          49202
Puebla              47079
Toluca              35366
Mexicali            33596
Xalapa              33187
Saltillo            33090
Chihuahua           30578
Campeche            30500
Villahermosa        16324
Santiago            15873
Ciudad Victoria     15866
San Luis Potosi     15499
Pachuca             14969
Morelia             14956
Merida              14875
Chetumal            14644
Culiacan            14594
Aguascalientes      14588
Tuxtla Gutierrez    14441
Chilpancingo        14438
Durango             13967
Oaxaca              13741
Cuernavaca          13335
Zacatecas           13264
La Paz              12924
Name: count, dtype: int64

Proportions (%):
Store_City
Cuidad de Mexico    10.931436
Guadalajara          8.932724
Monterrey            8.368225
Hermosillo           6.047532
Guanajuato           5.978154
Puebla               5.720205
Toluca               4.297049
Mexicali             4.081990
Xalapa               4.032295
Saltillo             4.020510
Chihuahua            3.715296
Campeche             3.705819
Villahermosa         1.983403
Santiago             1.928605
Ciudad Victoria      1.927755
San Luis Potosi      1.883163
Pachuca              1.818767
Morelia              1.817188
Merida               1.807346
Chetumal             1.779279
Culiacan             1.773204
Aguascalientes       1.772475
Tuxtla Gutierrez     1.754614
Chilpancingo         1.754250
Durango              1.697022
Oaxaca               1.669562
Cuernavaca           1.620233
Zacatecas            1.611606
La Paz               1.570295
Name: proportion, dtype: float64
------------------------------

--- Distribution for 'Store_Location' ---
Counts:
Store_Location
Downtown       476521
Commercial     183243
Residential     95621
Airport         67645
Name: count, dtype: int64

Proportions (%):
Store_Location
Downtown       57.898376
Commercial     22.264438
Residential    11.618167
Airport         8.219020
Name: proportion, dtype: float64
------------------------------

# Visualizing the product name distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Name'].value_counts() , palette = 'pastel' , edgecolor = 'black')

plt.title('Product name Distribution')
plt.xlabel('Product name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the product name distributions ( TOP 5)

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Name'].value_counts().head() , palette = 'pastel' , edgecolor = 'black')

plt.title('Product name Distribution - TOP 5 Products')
plt.xlabel('Product name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the product name distributions ( Bottom 5)

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Name'].value_counts().tail() , palette = 'pastel' , edgecolor = 'black')

plt.title('Product name Distribution - BOTTOM 5 Products')
plt.xlabel('Product name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the product category distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Product_Category'].value_counts() , palette = 'pastel' , edgecolor = 'black')

plt.title('Product category Distribution')
plt.xlabel('Product category')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the Store Name distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Store_Name'].value_counts() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store name Distribution')
plt.xlabel('Store name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the Store Name distributions - Top 5

plt.figure(figsize = (16,7))
sns.barplot(df['Store_Name'].value_counts().head() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store name Distribution - TOP 5')
plt.xlabel('Store name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the Store Name distributions - Bottom 5

plt.figure(figsize = (16,7))
sns.barplot(df['Store_Name'].value_counts().tail() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store name Distribution - BOTTOM 5')
plt.xlabel('Store name')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the store city distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Store_City'].value_counts() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store City Distribution')
plt.xlabel('Store City')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the store city distributions ( TOP 10)

plt.figure(figsize = (16,7))
sns.barplot(df['Store_City'].value_counts().head(10) , palette = 'pastel' , edgecolor = 'black')

plt.title('Store City Distribution - TOP 10')
plt.xlabel('Store City')
plt.ylabel('Count')
plt.xticks(rotation = 90)
plt.grid(False)
plt.show()

# Visualizing the store city distributions ( Bottom 5)

plt.figure(figsize = (18,7))
sns.barplot(df['Store_City'].value_counts().tail() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store City Distribution - BOTTOM 5')
plt.xlabel('Store City')
plt.ylabel('Count')
plt.grid(False)
plt.xticks(rotation = 45)
plt.grid(False)
plt.show()

# Visualizing the store location distributions

plt.figure(figsize = (16,7))
sns.barplot(df['Store_Location'].value_counts() , palette = 'pastel' , edgecolor = 'black')

plt.title('Store Location Distribution')
plt.xlabel('Store Location')
plt.ylabel('Count')
plt.grid(False)
plt.show()

# Define the utility function

def plot_categorical_donut(dataframe, column_name, n_categories=7, figsize=(10, 10)):
    """
    Generates and displays a donut chart for the top N categories of a specified column.

    Parameters:
    dataframe (pd.DataFrame): The input DataFrame.
    column_name (str): The name of the categorical column to plot.
    n_categories (int): The number of top categories to display explicitly.
                        Remaining categories will be grouped into 'Other'.
    figsize (tuple): A tuple (width, height) for the figure size.
    """
    if column_name not in dataframe.columns:
        print(f"Error: Column '{column_name}' not found in the DataFrame.")
        return

    # Calculate value counts
    counts = dataframe[column_name].value_counts()

    # Prepare data for plotting: select top N and sum the rest into 'Other'
    if len(counts) > n_categories:
        top_n = counts.head(n_categories)
        other_sum = counts.iloc[n_categories:].sum()
        if other_sum > 0:
            plot_data = pd.concat([top_n, pd.Series({'Other': other_sum})])
        else:
            plot_data = top_n
    else:
        plot_data = counts

    # Define colors
    colors = sns.color_palette('pastel', n_colors=len(plot_data))
    if 'Other' in plot_data.index:
        colors = sns.color_palette('pastel', n_colors=len(plot_data) - 1) + ['gray'] # Distinct color for 'Other'

    # Create figure and axis for the plot
    fig, ax = plt.subplots(figsize=figsize)

    # Plotting the donut chart
    wedges, texts, autotexts = ax.pie(
        plot_data,
        labels=plot_data.index,
        autopct='%1.1f%%',       # Format percentages
        startangle=90,           # Start at the top
        colors=colors,
        pctdistance=0.8,         # Position of percentage labels (closer to center of donut hole)
        labeldistance=1.05,      # Position of category labels (outside the donut)
        wedgeprops=dict(width=0.4, edgecolor='black', linewidth=1) # Donut hole via width
    )

    # Customize percentage labels
    for autotext in autotexts:
        autotext.set_color('black')
        autotext.set_fontsize(12)
        autotext.set_fontweight('bold')

    # Customize category labels
    for text in texts:
        text.set_fontsize(12)

    # Set title
    ax.set_title(f'Proportional Distribution of Top {n_categories} {column_name} Categories', fontsize=16, fontweight='bold')
    ax.axis('equal') # Ensure circle

    plt.tight_layout()
    plt.show()

# Donut Chart for Product_Category (Top 5)

plot_categorical_donut(df, 'Product_Category', n_categories=5 , figsize=(10,8))

# Donut Chart for Store_City (Top 5)

plot_categorical_donut(df, 'Store_Name', n_categories=10, figsize=(10,8))

# Donut Chart for Store_City (Top 5)

plot_categorical_donut(df, 'Store_City', n_categories=10, figsize=(10,8))

# Donut Chart for Store_Location (All Categories)

plot_categorical_donut(df, 'Store_Location', n_categories=df['Store_Location'].nunique() , figsize=(10,8))

# Example for a column with many unique values, showing only top 10
# Donut Chart for Product_Name (Top 10)

plot_categorical_donut(df, 'Product_Name', n_categories=10, figsize=(10,8))

# Temporal variables - Date , Store_Open_Date

df.head()

# Assume 'df' DataFrame is already loaded and contains the specified columns,
# especially the 'Date' column as datetime64[ns].

# --- 1. What is the date range of the dataset? ---
print("--- 1. Date Range of the Dataset ---")
min_date = df['Date'].min()
max_date = df['Date'].max()

print(f"  Start Date: {min_date.strftime('%Y-%m-%d')}")
print(f"  End Date:   {max_date.strftime('%Y-%m-%d')}")

--- 1. Date Range of the Dataset ---
  Start Date: 2022-01-01
  End Date:   2023-09-30

df.shape

(823030, 16)

# Define the Utility function to aggregate the data

def aggregate_kpis_by_frequency(
    df: pd.DataFrame,
    date_column: str,
    aggregation_frequency: str,
    kpis: list
):
    """
    Aggregates specified KPIs by a given time frequency (Week, Month, Quarter, Half-Year, Year).

    Args:
        df (pd.DataFrame): The input DataFrame.
        date_column (str): The name of the date column (must be datetime64[ns]).
        aggregation_frequency (str): The frequency for aggregation.
                                     Accepted values: 'W' (Weekly), 'M' (Monthly),
                                     'Q' (Quarterly)
        kpis (list): A list of KPI column names to aggregate (e.g., ['Units', 'Revenue', 'Profit']).

    Returns:
        pd.DataFrame: A DataFrame aggregated by the specified frequency, containing the sum
                      of the specified KPIs. Returns an empty DataFrame if inputs are invalid.
    """

    # --- Input Validation ---
    if not pd.api.types.is_datetime64_any_dtype(df[date_column]):
        print(f"Error: '{date_column}' column is not in datetime format. Please convert it first.")
        return pd.DataFrame()

    for kpi in kpis:
        if kpi not in df.columns:
            print(f"Error: KPI column '{kpi}' not found in DataFrame.")
            return pd.DataFrame()

    valid_frequencies = {'W', 'M', 'Q'}
    if aggregation_frequency.upper() not in valid_frequencies:
        print(f"Error: Invalid aggregation_frequency '{aggregation_frequency}'. "
              f"Accepted values are {', '.join(valid_frequencies)}.")
        return pd.DataFrame()

    # --- Data Preparation ---
    df_selected = df[[date_column] + kpis].copy()
    daily_agg_df = df_selected.groupby(df_selected[date_column].dt.date)[kpis].sum().reset_index()
    daily_agg_df[date_column] = pd.to_datetime(daily_agg_df[date_column])
    daily_agg_df = daily_agg_df.set_index(date_column)

    # --- Resampling and Aggregation ---
    if aggregation_frequency.upper() == 'W':
        resampled_df = daily_agg_df.resample('W')[kpis].sum()
        date_col_name = 'Week_End_Date'
    elif aggregation_frequency.upper() == 'M':
        resampled_df = daily_agg_df.resample('M')[kpis].sum()
        date_col_name = 'Month_End_Date'
    elif aggregation_frequency.upper() == 'Q':
        resampled_df = daily_agg_df.resample('Q')[kpis].sum()
        date_col_name = 'Quarter_End_Date'
    else:
        print(f"Internal Error: Unhandled aggregation_frequency '{aggregation_frequency}'.")
        return pd.DataFrame()

    resampled_df = resampled_df.reset_index()
    resampled_df.rename(columns={date_column: date_col_name}, inplace=True)

    return resampled_df

# Define KPIs to aggregate

kpis_to_aggregate = ['Units', 'Revenue', 'Profit']

df.head()

# --- Aggregate Weekly output ---

print("\n--- Weekly Aggregation (Units, Revenue, Profit) ---")
weekly_agg_df = aggregate_kpis_by_frequency(df.copy(), 'Date', 'W', kpis_to_aggregate)
print(weekly_agg_df)
print(f"Total weeks: {len(weekly_agg_df)}")

--- Weekly Aggregation (Units, Revenue, Profit) ---
   Week_End_Date  Units    Revenue   Profit
0     2022-01-02   2891   40827.09  12843.0
1     2022-01-09   8827  128218.73  39223.0
2     2022-01-16   8089  112999.11  35413.0
3     2022-01-23   8184  120047.16  35805.0
4     2022-01-30   9013  125938.87  38881.0
..           ...    ...        ...      ...
87    2023-09-03  11958  157664.42  43081.0
88    2023-09-10  10645  136453.55  37936.0
89    2023-09-17  11892  148422.08  41638.0
90    2023-09-24  11817  148564.83  40692.0
91    2023-10-01  11375  140443.25  37605.0

[92 rows x 4 columns]
Total weeks: 92

# --- Aggregate Monthly Output ---

print("\n--- Monthly Aggregation (Units, Revenue, Profit) ---")
monthly_agg_df = aggregate_kpis_by_frequency(df.copy(), 'Date', 'M', kpis_to_aggregate)
print(monthly_agg_df)
print(f"Total months: {len(monthly_agg_df)}")

--- Monthly Aggregation (Units, Revenue, Profit) ---
   Month_End_Date  Units    Revenue    Profit
0      2022-01-31  37971  541800.29  166700.0
1      2022-02-28  36894  540867.06  161709.0
2      2022-03-31  39970  589375.30  173970.0
3      2022-04-30  47097  681033.03  190079.0
4      2022-05-31  46524  668055.76  184017.0
5      2022-06-30  46659  656675.41  186120.0
6      2022-07-31  45469  548981.31  171996.0
7      2022-08-31  39243  482599.57  154283.0
8      2022-09-30  41747  581358.53  163254.0
9      2022-10-31  47351  618680.49  175216.0
10     2022-11-30  50902  658413.98  190939.0
11     2022-12-31  65472  870680.28  242133.0
12     2023-01-31  56032  742536.68  202037.0
13     2023-02-28  54330  720125.70  187618.0
14     2023-03-31  68990  879709.10  229379.0
15     2023-04-30  65242  824184.58  212639.0
16     2023-05-31  63504  821852.96  207918.0
17     2023-06-30  63911  804662.89  204664.0
18     2023-07-31  63357  825781.43  208008.0
19     2023-08-31  51104  659987.96  174415.0
20     2023-09-30  52122  657894.78  180235.0
Total months: 21

# --- Aggregate Quarterly Output ---

print("\n--- Quarterly Aggregation (Units, Revenue, Profit) ---")
quarterly_agg_df = aggregate_kpis_by_frequency(df.copy(), 'Date', 'Q', kpis_to_aggregate)
print(quarterly_agg_df) # Print all for quarterly as it's a smaller dataset
print(f"Total quarters: {len(quarterly_agg_df)}")

--- Quarterly Aggregation (Units, Revenue, Profit) ---
  Quarter_End_Date   Units     Revenue    Profit
0       2022-03-31  114835  1672042.65  502379.0
1       2022-06-30  140280  2005764.20  560216.0
2       2022-09-30  126459  1612939.41  489533.0
3       2022-12-31  163725  2147774.75  608288.0
4       2023-03-31  179352  2342371.48  619034.0
5       2023-06-30  192657  2450700.43  625221.0
6       2023-09-30  166583  2143664.17  562658.0
Total quarters: 7

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from typing import List

def plot_kpi_multiline_chart(
    df: pd.DataFrame,
    x_column: str,
    kpi_columns: List[str],
    title: str = "KPI Trends Over Time",
    y_label: str = "Value",
    colors: dict = None,
    add_zero_line: bool = False,
    y_lim: tuple = None,
    marker_style: str = 'o',
    line_width: float = 2.5
):
    """
    Creates a multiline chart to visualize trends of multiple KPIs over time.

    Args:
        df (pd.DataFrame): The DataFrame containing the time-series data and KPIs.
                           Assumes the DataFrame is already aggregated (e.g., weekly, monthly).
        x_column (str): The name of the column to use for the x-axis (e.g., 'Week_End_Date', 'Month_End_Date').
                        This column should be in datetime format or suitable for plotting on an axis.
        kpi_columns (List[str]): A list of KPI column names to plot as separate lines.
                                 (e.g., ['Units', 'Revenue', 'Profit']).
        title (str, optional): The main title of the chart. Defaults to "KPI Trends Over Time".
        y_label (str, optional): The label for the y-axis. Defaults to "Value".
        colors (dict, optional): A dictionary mapping KPI column names to desired colors.
                                 If None, seaborn's default color palette will be used.
                                 Example: {'Units': 'blue', 'Revenue': 'green', 'Profit': 'orange'}.
        add_zero_line (bool, optional): If True, adds a horizontal line at y=0. Useful for growth plots.
                                        Defaults to False.
        y_lim (tuple, optional): A tuple (min_val, max_val) to set explicit y-axis limits.
                                 If None, limits are automatically determined.
        marker_style (str, optional): Matplotlib marker style for data points. Defaults to 'o'.
        line_width (float, optional): Line width for the plots. Defaults to 2.5.
    """

    if not all(col in df.columns for col in kpi_columns):
        missing_kpis = [col for col in kpi_columns if col not in df.columns]
        print(f"Error: The following KPI columns were not found in the DataFrame: {missing_kpis}")
        return
    if x_column not in df.columns:
        print(f"Error: X-axis column '{x_column}' not found in the DataFrame.")
        return

    # Ensure x_column is treated as datetime for proper plotting if it's date-like string
    if pd.api.types.is_object_dtype(df[x_column]) or pd.api.types.is_string_dtype(df[x_column]):
        try:
            df[x_column] = pd.to_datetime(df[x_column])
        except Exception as e:
            print(f"Warning: Could not convert '{x_column}' to datetime. Plotting as is. Error: {e}")

    # Set a nice seaborn style
    sns.set_style("whitegrid")

    # Create the figure and axes
    fig, ax = plt.subplots(figsize=(20, 8)) # Adjusted size for better visibility

    # Plot each KPI
    for i, kpi in enumerate(kpi_columns):
        if colors and kpi in colors:
            line_color = colors[kpi]
        else:
            # Use seaborn's default palette if no custom colors are provided
            line_color = sns.color_palette()[i % len(sns.color_palette())]

        ax.plot(df[x_column], df[kpi], marker=marker_style, color=line_color,
                label=kpi, linewidth=line_width)

    # Add labels and title
    ax.set_xlabel(x_column.replace('_', ' ').title(), fontsize=14) # Auto-format label
    ax.set_ylabel(y_label, fontsize=14)
    ax.set_title(title, fontsize=18, weight='bold')

    # Add horizontal line at y=0 if requested (useful for growth percentages)
    if add_zero_line:
        ax.axhline(0, color='gray', linestyle='--', linewidth=0.8)

    # Set y-axis limits if provided
    if y_lim:
        ax.set_ylim(y_lim)
        # Optional: set y-ticks based on y_lim, can make more generic or remove
        # if y_label includes "%", format ticks as percentages
        if '%' in y_label:
            ax.set_yticks(np.arange(y_lim[0], y_lim[1] + 1, (y_lim[1] - y_lim[0]) / 4)) # Example for 4 ticks
            ax.set_yticklabels([f'{int(y)}%' for y in ax.get_yticks()])


    # Customize plot appearance
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.tick_params(axis='x', labelsize=12, rotation=45) # Rotate x-labels
    ax.tick_params(axis='y', labelsize=12)
    ax.grid(False) # Ensure grid is visible

    # Add legend
    # Place legend outside the plot to avoid obscuring lines, or adjust its position.
    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=12, frameon=False)

    plt.tight_layout(rect=[0, 0, 0.85, 1]) # Adjust layout to prevent legend from overlapping
    plt.show()

# Example 1: Plotting Weekly Units, Revenue, and Profit


plot_kpi_multiline_chart(
    df=weekly_agg_df,
    x_column='Week_End_Date',
    kpi_columns=['Units', 'Revenue', 'Profit'],
    title='Weekly Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta', 'Revenue': 'green', 'Profit': 'orange'}
)

# Example2 : Plotting Weekly Unit Sales


plot_kpi_multiline_chart(
    df=weekly_agg_df,
    x_column='Week_End_Date',
    kpi_columns=['Units'],
    title='Weekly Unit Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta'}
)

# Example3 : Plotting Weekly Revenue


plot_kpi_multiline_chart(
    df=weekly_agg_df,
    x_column='Week_End_Date',
    kpi_columns=['Revenue'],
    title='Weekly Revenue  Performance - Actuals',
    y_label='Total Value',
    colors={'Revenue': 'green'}
)

# Example4 : Plotting Weekly Profit


plot_kpi_multiline_chart(
    df=weekly_agg_df,
    x_column='Week_End_Date',
    kpi_columns=['Profit'],
    title='Weekly Profit  Performance - Actuals',
    y_label='Total Value',
    colors={'Profit': 'orange'}
)

# Example1 : Plotting Monthly Units, Revenue, and Profit


plot_kpi_multiline_chart(
    df=monthly_agg_df,
    x_column='Month_End_Date',
    kpi_columns=['Units', 'Revenue', 'Profit'],
    title='Monthly Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta', 'Revenue': 'green', 'Profit': 'orange'}
)

# Example 2: Plotting Monthly Units


plot_kpi_multiline_chart(
    df=monthly_agg_df,
    x_column='Month_End_Date',
    kpi_columns=['Units'],
    title='Monthly Unit Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta'}
)

# Example 3: Plotting Monthly Revenue


plot_kpi_multiline_chart(
    df=monthly_agg_df,
    x_column='Month_End_Date',
    kpi_columns=['Revenue'],
    title='Monthly Revenue Performance - Actuals',
    y_label='Total Value',
    colors={'Revenue': 'green'}
)

# Example 4: Plotting Monthly Profit


plot_kpi_multiline_chart(
    df=monthly_agg_df,
    x_column='Month_End_Date',
    kpi_columns=['Profit'],
    title='Monthly Profit Performance - Actuals',
    y_label='Total Value',
    colors={'Profit': 'orange'}
)

# Example 1: Plotting Quarterly Units, Revenue, and Profit


plot_kpi_multiline_chart(
    df=quarterly_agg_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Units', 'Revenue', 'Profit'],
    title='Quarterly Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta', 'Revenue': 'green', 'Profit': 'orange'}
)

# Example 2: Plotting Quarterly Units


plot_kpi_multiline_chart(
    df=quarterly_agg_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Units'],
    title='Quarterly Unit Sales Performance - Actuals',
    y_label='Total Value',
    colors={'Units': 'magenta'}
)

# Example 3: Plotting Quarterly Revenue


plot_kpi_multiline_chart(
    df=quarterly_agg_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Revenue'],
    title='Quarterly Revenue Performance - Actuals',
    y_label='Total Value',
    colors={'Revenue': 'green'}
)

# Example 4: Plotting Quarterly Revenue


plot_kpi_multiline_chart(
    df=quarterly_agg_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Profit'],
    title='Quarterly Profit Performance - Actuals',
    y_label='Total Value',
    colors={'Profit': 'orange'}
)

def calculate_period_over_period_growth(
    aggregated_df: pd.DataFrame,
    date_column: str,
    kpis: List[str],
    period_name: str # e.g., 'WoW', 'MoM', 'QoQ'
) -> pd.DataFrame:
    """
    Calculates period-over-period growth for specified KPIs in an already aggregated DataFrame.

    Args:
        aggregated_df (pd.DataFrame): The DataFrame already aggregated by week, month, or quarter.
                                      Must contain the date_column and all kpis.
        date_column (str): The name of the date column in the aggregated_df (e.g., 'Week_End_Date').
                           Must be in datetime format.
        kpis (List[str]): A list of KPI column names for which to calculate growth.
        period_name (str): A string indicating the period type (e.g., 'WoW', 'MoM', 'QoQ').
                           Used for naming the new growth columns.

    Returns:
        pd.DataFrame: A new DataFrame containing the date column, the original KPI values,
                      and the calculated growth percentages for each KPI.
                      Returns an empty DataFrame if inputs are invalid.
    """
    if not pd.api.types.is_datetime64_any_dtype(aggregated_df[date_column]):
        print(f"Error: '{date_column}' column in aggregated_df is not in datetime format. Please ensure it is.")
        return pd.DataFrame()

    for kpi in kpis:
        if kpi not in aggregated_df.columns:
            print(f"Error: KPI column '{kpi}' not found in the aggregated DataFrame.")
            return pd.DataFrame()

    # Create a copy to avoid modifying the original aggregated_df
    growth_df = aggregated_df.copy()

    # Sort by date column to ensure correct pct_change calculation
    growth_df = growth_df.sort_values(by=date_column).reset_index(drop=True)

    # Calculate growth for each KPI
    for kpi in kpis:
        growth_column_name = f'{kpi}_{period_name}_Growth_pct'
        growth_df[growth_column_name] = growth_df[kpi].pct_change() * 100

    # Drop rows where any of the new growth columns are NaN (typically the very first period)
    # This ensures that incomplete first periods don't show NaN growth.
    growth_columns_to_drop_na = [f'{kpi}_{period_name}_Growth_pct' for kpi in kpis]
    growth_df = growth_df.dropna(subset=growth_columns_to_drop_na).reset_index(drop=True)

    # Select and return relevant columns: date, original KPIs, and new growth KPIs
    output_columns = [date_column] + kpis + growth_columns_to_drop_na
    return growth_df[output_columns]

# Define common KPIs

kpis_to_analyze = ['Units', 'Revenue', 'Profit']

# 2. Calculate WoW Growth

wow_growth_df = calculate_period_over_period_growth(
    aggregated_df=weekly_agg_df,
    date_column='Week_End_Date',
    kpis=kpis_to_analyze,
    period_name='WoW'
)
print(wow_growth_df)
print(f"Total rows: {len(wow_growth_df)}")

   Week_End_Date  Units    Revenue   Profit  Units_WoW_Growth_pct  \
0     2022-01-09   8827  128218.73  39223.0            205.326877   
1     2022-01-16   8089  112999.11  35413.0             -8.360711   
2     2022-01-23   8184  120047.16  35805.0              1.174434   
3     2022-01-30   9013  125938.87  38881.0             10.129521   
4     2022-02-06   9577  136330.23  40724.0              6.257628   
..           ...    ...        ...      ...                   ...   
86    2023-09-03  11958  157664.42  43081.0             10.323831   
87    2023-09-10  10645  136453.55  37936.0            -10.980097   
88    2023-09-17  11892  148422.08  41638.0             11.714420   
89    2023-09-24  11817  148564.83  40692.0             -0.630676   
90    2023-10-01  11375  140443.25  37605.0             -3.740374   

    Revenue_WoW_Growth_pct  Profit_WoW_Growth_pct  
0               214.053071             205.403722  
1               -11.870044              -9.713688  
2                 6.237262               1.106938  
3                 4.907830               8.590979  
4                 8.251114               4.740104  
..                     ...                    ...  
86               13.885962              10.111182  
87              -13.453175             -11.942620  
88                8.771139               9.758541  
89                0.096178              -2.271963  
90               -5.466691              -7.586258  

[91 rows x 7 columns]
Total rows: 91

# 2. Calculate MoM Growth


mom_growth_df = calculate_period_over_period_growth(
    aggregated_df=monthly_agg_df,
    date_column='Month_End_Date',
    kpis=kpis_to_analyze,
    period_name='MoM'
)
print(mom_growth_df)
print(f"Total rows: {len(mom_growth_df)}")

   Month_End_Date  Units    Revenue    Profit  Units_MoM_Growth_pct  \
0      2022-02-28  36894  540867.06  161709.0             -2.836375   
1      2022-03-31  39970  589375.30  173970.0              8.337399   
2      2022-04-30  47097  681033.03  190079.0             17.830873   
3      2022-05-31  46524  668055.76  184017.0             -1.216638   
4      2022-06-30  46659  656675.41  186120.0              0.290173   
5      2022-07-31  45469  548981.31  171996.0             -2.550419   
6      2022-08-31  39243  482599.57  154283.0            -13.692846   
7      2022-09-30  41747  581358.53  163254.0              6.380756   
8      2022-10-31  47351  618680.49  175216.0             13.423719   
9      2022-11-30  50902  658413.98  190939.0              7.499314   
10     2022-12-31  65472  870680.28  242133.0             28.623630   
11     2023-01-31  56032  742536.68  202037.0            -14.418377   
12     2023-02-28  54330  720125.70  187618.0             -3.037550   
13     2023-03-31  68990  879709.10  229379.0             26.983251   
14     2023-04-30  65242  824184.58  212639.0             -5.432671   
15     2023-05-31  63504  821852.96  207918.0             -2.663928   
16     2023-06-30  63911  804662.89  204664.0              0.640905   
17     2023-07-31  63357  825781.43  208008.0             -0.866830   
18     2023-08-31  51104  659987.96  174415.0            -19.339615   
19     2023-09-30  52122  657894.78  180235.0              1.992016   

    Revenue_MoM_Growth_pct  Profit_MoM_Growth_pct  
0                -0.172246              -2.994001  
1                 8.968607               7.582138  
2                15.551675               9.259642  
3                -1.905527              -3.189200  
4                -1.703503               1.142829  
5               -16.399898              -7.588652  
6               -12.091803             -10.298495  
7                20.463955               5.814639  
8                 6.419784               7.327232  
9                 6.422296               8.973496  
10               32.239033              26.811704  
11              -14.717641             -16.559494  
12               -3.018165              -7.136812  
13               22.160492              22.258525  
14               -6.311691              -7.297965  
15               -0.282900              -2.220195  
16               -2.091624              -1.565040  
17                2.624520               1.633898  
18              -20.077161             -16.149860  
19               -0.317154               3.336869  
Total rows: 20

# 3. Calculate QoQ Growth

qoq_growth_df = calculate_period_over_period_growth(
    aggregated_df=quarterly_agg_df,
    date_column='Quarter_End_Date',
    kpis=kpis_to_analyze,
    period_name='QoQ'
)
print(qoq_growth_df) # Print all for quarterly as it's typically fewer rows
print(f"Total rows: {len(qoq_growth_df)}")

  Quarter_End_Date   Units     Revenue    Profit  Units_QoQ_Growth_pct  \
0       2022-06-30  140280  2005764.20  560216.0             22.157879   
1       2022-09-30  126459  1612939.41  489533.0             -9.852438   
2       2022-12-31  163725  2147774.75  608288.0             29.468840   
3       2023-03-31  179352  2342371.48  619034.0              9.544663   
4       2023-06-30  192657  2450700.43  625221.0              7.418373   
5       2023-09-30  166583  2143664.17  562658.0            -13.533897   

   Revenue_QoQ_Growth_pct  Profit_QoQ_Growth_pct  
0               19.958914              11.512623  
1              -19.584794             -12.617098  
2               33.159047              24.258834  
3                9.060388               1.766597  
4                4.624755               0.999460  
5              -12.528510             -10.006542  
Total rows: 6

# Example 1: Plotting Quarterly Units, Revenue, and Profit growth


plot_kpi_multiline_chart(
    df=qoq_growth_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Units_QoQ_Growth_pct' , 'Revenue_QoQ_Growth_pct' , 'Profit_QoQ_Growth_pct'],
    title='Quarterly  Sales Performance - growth',
    y_label='Growth %',
    colors={'Units_QoQ_Growth_pct': 'magenta' , 'Revenue_QoQ_Growth_pct': 'green' , 'Profit_QoQ_Growth_pct': 'orange'}
)

# Example 2: Plotting Quarterly Units growth


plot_kpi_multiline_chart(
    df=qoq_growth_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Units_QoQ_Growth_pct'],
    title='Quarterly  Unit Sales Performance - growth',
    y_label='Growth %',
    colors={'Units_QoQ_Growth_pct': 'magenta' }
)

# Example 3: Plotting Quarterly Profit growth


plot_kpi_multiline_chart(
    df=qoq_growth_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Profit_QoQ_Growth_pct'],
    title='Quarterly Profit Performance - growth',
    y_label='Growth %',
    colors={'Profit_QoQ_Growth_pct': 'orange'}
)

# Example 4: Plotting Quarterly Revenue growth


plot_kpi_multiline_chart(
    df=qoq_growth_df,
    x_column='Quarter_End_Date',
    kpi_columns=['Revenue_QoQ_Growth_pct'],
    title='Quarterly Revenue Performance - growth',
    y_label='Growth %',
    colors={'Revenue_QoQ_Growth_pct': 'green'}
)

# Example 1: Plotting Monthly Units, Revenue, and Profit growth


plot_kpi_multiline_chart(
    df=mom_growth_df,
    x_column='Month_End_Date',
    kpi_columns=['Units_MoM_Growth_pct', 'Revenue_MoM_Growth_pct', 'Profit_MoM_Growth_pct'],
    title='Monthly Sales Performance - Growth',
    y_label='Growth %',
    colors={'Units_MoM_Growth_pct': 'magenta', 'Revenue_MoM_Growth_pct': 'green', 'Profit_MoM_Growth_pct': 'orange'}
)

# Example 2: Plotting Monthly Units growth


plot_kpi_multiline_chart(
    df=mom_growth_df,
    x_column='Month_End_Date',
    kpi_columns=['Units_MoM_Growth_pct'],
    title='Monthly Unit Sales Performance - Growth',
    y_label='Growth %',
    colors={'Units_MoM_Growth_pct': 'magenta'}
)

# Example 3: Plotting Monthly Revenue growth


plot_kpi_multiline_chart(
    df=mom_growth_df,
    x_column='Month_End_Date',
    kpi_columns=['Revenue_MoM_Growth_pct'],
    title='Monthly Revenue Performance - Growth',
    y_label='Growth %',
    colors={ 'Revenue_MoM_Growth_pct': 'green'}
)

# Example 4: Plotting Monthly Profit growth


plot_kpi_multiline_chart(
    df=mom_growth_df,
    x_column='Month_End_Date',
    kpi_columns=['Profit_MoM_Growth_pct'],
    title='Monthly Profit Performance - Growth',
    y_label='Growth %',
    colors={'Profit_MoM_Growth_pct': 'orange'}
)

# Since the 1st week in 2022 ends on 2nd jan we are removing the 1st growth since its comparison between 2 days and 7 days

wow_growth_df_modified = wow_growth_df.iloc[1:].copy()

# Example 1: Plotting Weekly Units, Revenue, and Profit growth


plot_kpi_multiline_chart(
    df=wow_growth_df_modified,
    x_column='Week_End_Date',
    kpi_columns=['Units_WoW_Growth_pct', 'Revenue_WoW_Growth_pct', 'Profit_WoW_Growth_pct'],
    title='Weely Sales Performance - Growth',
    y_label='Growth %',
    colors={'Units_WoW_Growth_pct': 'magenta', 'Revenue_WoW_Growth_pct': 'green', 'Profit_WoW_Growth_pct': 'orange'}
)

# Example 2: Plotting Weekly Units growth


plot_kpi_multiline_chart(
    df=wow_growth_df_modified,
    x_column='Week_End_Date',
    kpi_columns=['Units_WoW_Growth_pct'],
    title='Weely Unit Sales Performance - Growth',
    y_label='Growth %',
    colors={'Units_WoW_Growth_pct': 'magenta'}
)

# Example 3: Plotting Weekly Revenue growth


plot_kpi_multiline_chart(
    df=wow_growth_df_modified,
    x_column='Week_End_Date',
    kpi_columns=['Revenue_WoW_Growth_pct'],
    title='Weely Revenue Performance - Growth',
    y_label='Growth %',
    colors={'Revenue_WoW_Growth_pct': 'green'}
)

# Example 4: Plotting Weekly Profit growth


plot_kpi_multiline_chart(
    df=wow_growth_df_modified,
    x_column='Week_End_Date',
    kpi_columns=['Profit_WoW_Growth_pct'],
    title='Weely Profit Performance - Growth',
    y_label='Growth %',
    colors={'Profit_WoW_Growth_pct': 'orange'}
)

# Product_Name , Product_Category , Store_Name , Store_City , Store_Location

print((df.groupby('Product_Name').agg({'Units':'sum'}).sort_values(by='Units', ascending=False)).head(10))
print((df.groupby('Product_Name').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False)).head(10))
print((df.groupby('Product_Name').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False)).head(10))
print((df.groupby('Product_Name').agg({'Units':'sum'}).sort_values(by='Units', ascending=False)).tail(10))
print((df.groupby('Product_Name').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False)).tail(10))
print((df.groupby('Product_Name').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False)).tail(10))

                  Units
Product_Name           
Colorbuds        104368
PlayDoh Can      103128
Barrel O' Slime   91663
Deck Of Cards     84034
Magic Sand        60598
Splash Balls      60248
Lego Bricks       59737
Action Figure     57958
Rubik's Cube      45672
Animal Figures    39089
                   Revenue
Product_Name              
Lego Bricks     2388882.63
Colorbuds       1564476.32
Magic Sand       968962.02
Action Figure    926748.42
Rubik's Cube     912983.28
Deck Of Cards    587397.66
Splash Balls     541629.52
Nerf Gun         530594.57
Animal Figures   507766.11
Dart Gun         505092.12
                   Profit
Product_Name             
Colorbuds        834944.0
Action Figure    347748.0
Lego Bricks      298685.0
Deck Of Cards    252102.0
Glass Marbles    187590.0
Barrel O' Slime  183326.0
Kids Makeup Kit  146598.0
Nerf Gun         132715.0
Dart Gun         126352.0
Etch A Sketch    121680.0
                       Units
Product_Name                
Supersoaker Water Gun   6793
Teddy Bear              6416
PlayDoh Playset         6298
Plush Pony              5438
Classic Dominoes        4348
Playfoam                4158
Chutes & Ladders        3776
Monopoly                3321
Uno Card Game           2654
Mini Basketball Hoop    2550
                       Revenue
Product_Name                  
Foam Disk Launcher    91363.80
Mr. Potatohead        85804.11
Teddy Bear            83343.84
Jenga                 68321.61
Monopoly              66386.79
Mini Basketball Hoop  63724.50
Chutes & Ladders      49050.24
Playfoam              45696.42
Classic Dominoes      43436.52
Uno Card Game         21205.46
                        Profit
Product_Name                  
Playfoam               29106.0
PlayDoh Playset        25192.0
PlayDoh Toolkit        23128.0
Foam Disk Launcher     22860.0
Supersoaker Water Gun  20379.0
Monopoly               19926.0
Teddy Bear             12832.0
Chutes & Ladders       11328.0
Uno Card Game          10616.0
Classic Dominoes        8696.0

print(df.groupby('Product_Category').agg({'Units':'sum'}).sort_values(by='Units', ascending=False))
print(df.groupby('Product_Category').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False))
print(df.groupby('Product_Category').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False))

                    Units
Product_Category         
Art & Crafts       325574
Toys               267134
Games              188162
Sports & Outdoors  168946
Electronics        134075
                      Revenue
Product_Category             
Toys               5092081.66
Art & Crafts       2705364.26
Electronics        2246771.25
Sports & Outdoors  2169935.54
Games              2161104.38
                      Profit
Product_Category            
Toys               1078897.0
Electronics        1001437.0
Art & Crafts        753354.0
Games               629475.0
Sports & Outdoors   504166.0

print((df.groupby('Store_Name').agg({'Units':'sum'}).sort_values(by='Units', ascending=False).head(10)))
print((df.groupby('Store_Name').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False).head(10)))
print((df.groupby('Store_Name').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False).head(10)))
print((df.groupby('Store_Name').agg({'Units':'sum'}).sort_values(by='Units', ascending=False).tail(10)))
print((df.groupby('Store_Name').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False).tail(10)))
print((df.groupby('Store_Name').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False).tail(10)))

                               Units
Store_Name                          
Maven Toys Ciudad de Mexico 2  42086
Maven Toys Ciudad de Mexico 1  33479
Maven Toys Toluca 1            32066
Maven Toys Guadalajara 3       31299
Maven Toys Monterrey 2         28318
Maven Toys Ciudad de Mexico 3  26634
Maven Toys Xalapa 2            24869
Maven Toys Guanajuato 1        24722
Maven Toys Campeche 1          24556
Maven Toys Saltillo 1          24010
                                 Revenue
Store_Name                              
Maven Toys Ciudad de Mexico 2  547850.14
Maven Toys Guadalajara 3       446258.01
Maven Toys Ciudad de Mexico 1  433556.21
Maven Toys Toluca 1            405143.34
Maven Toys Monterrey 2         372998.82
Maven Toys Guadalajara 4       345729.38
Maven Toys Hermosillo 3        344846.64
Maven Toys Xalapa 2            340041.31
Maven Toys Ciudad de Mexico 3  337424.66
Maven Toys Saltillo 1          330408.90
                                 Profit
Store_Name                             
Maven Toys Ciudad de Mexico 2  165159.0
Maven Toys Guadalajara 3       119401.0
Maven Toys Ciudad de Mexico 1  111296.0
Maven Toys Monterrey 2         106783.0
Maven Toys Toluca 1            100398.0
Maven Toys Guadalajara 4       100260.0
Maven Toys Hermosillo 3         98825.0
Maven Toys Mexicali 1           97206.0
Maven Toys Saltillo 1           94252.0
Maven Toys Ciudad de Mexico 3   94021.0
                             Units
Store_Name                        
Maven Toys Merida 1          18328
Maven Toys Chilpancingo 1    18146
Maven Toys Chihuahua 1       18070
Maven Toys Aguascalientes 1  17865
Maven Toys Durango 1         17279
Maven Toys Zacatecas 1       16959
Maven Toys Cuernavaca 1      16741
Maven Toys Campeche 2        16277
Maven Toys La Paz 1          15924
Maven Toys Toluca 2          15568
                                 Revenue
Store_Name                              
Maven Toys Pachuca 1           237676.15
Maven Toys Hermosillo 1        234495.80
Maven Toys Merida 1            232097.72
Maven Toys Tuxtla Gutierrez 1  227662.04
Maven Toys Zacatecas 1         227615.41
Maven Toys Durango 1           220890.21
Maven Toys Cuernavaca 1        217990.59
Maven Toys Toluca 2            217353.32
Maven Toys La Paz 1            207520.76
Maven Toys Campeche 2          206055.23
                          Profit
Store_Name                      
Maven Toys Merida 1      64399.0
Maven Toys Pachuca 1     63989.0
Maven Toys Culiacan 1    63959.0
Maven Toys Durango 1     61672.0
Maven Toys Campeche 2    58091.0
Maven Toys Oaxaca 1      57987.0
Maven Toys Zacatecas 1   57501.0
Maven Toys Toluca 2      55539.0
Maven Toys La Paz 1      55406.0
Maven Toys Cuernavaca 1  54425.0

print(df.groupby('Store_City').agg({'Units':'sum'}).sort_values(by='Units', ascending=False).head(10))
print(df.groupby('Store_City').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False).head(10))
print(df.groupby('Store_City').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False).head(10))
print(df.groupby('Store_City').agg({'Units':'sum'}).sort_values(by='Units', ascending=False).tail(10))
print(df.groupby('Store_City').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False).tail(10))
print(df.groupby('Store_City').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False).tail(10))

                   Units
Store_City              
Cuidad de Mexico  124684
Guadalajara        95568
Monterrey          93229
Hermosillo         66754
Guanajuato         64299
Puebla             60808
Toluca             47634
Mexicali           44156
Xalapa             43567
Saltillo           42903
                     Revenue
Store_City                  
Cuidad de Mexico  1640351.16
Guadalajara       1313248.32
Monterrey         1256741.71
Hermosillo         902769.46
Guanajuato         868876.01
Puebla             804583.92
Toluca             622496.66
Xalapa             603566.33
Mexicali           583588.44
Saltillo           579514.97
                    Profit
Store_City                
Cuidad de Mexico  459153.0
Guadalajara       362728.0
Monterrey         343209.0
Hermosillo        263174.0
Guanajuato        234921.0
Puebla            226985.0
Mexicali          173235.0
Saltillo          163248.0
Xalapa            159128.0
Toluca            155937.0
                Units
Store_City           
Pachuca         19185
Chetumal        18965
Oaxaca          18331
Merida          18328
Chilpancingo    18146
Aguascalientes  17865
Durango         17279
Zacatecas       16959
Cuernavaca      16741
La Paz          15924
                    Revenue
Store_City                 
Chilpancingo      240731.54
Aguascalientes    239997.35
Oaxaca            239826.69
Pachuca           237676.15
Merida            232097.72
Tuxtla Gutierrez  227662.04
Zacatecas         227615.41
Durango           220890.21
Cuernavaca        217990.59
La Paz            207520.76
                   Profit
Store_City               
Chilpancingo      65291.0
Tuxtla Gutierrez  64531.0
Merida            64399.0
Pachuca           63989.0
Culiacan          63959.0
Durango           61672.0
Oaxaca            57987.0
Zacatecas         57501.0
La Paz            55406.0
Cuernavaca        54425.0

print(df.groupby('Store_Location').agg({'Units':'sum'}).sort_values(by='Units', ascending=False))
print(df.groupby('Store_Location').agg({'Revenue':'sum'}).sort_values(by='Revenue', ascending=False))
print(df.groupby('Store_Location').agg({'Profit':'sum'}).sort_values(by='Profit', ascending=False))

                 Units
Store_Location        
Downtown        624180
Commercial      239103
Residential     125147
Airport          95461
                   Revenue
Store_Location            
Downtown        8179717.20
Commercial      3262550.97
Residential     1653066.53
Airport         1279922.39
                   Profit
Store_Location           
Downtown        2221455.0
Commercial       916257.0
Residential      458435.0
Airport          371182.0

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from typing import Literal

def plot_aggregated_barplot(
    df: pd.DataFrame,
    aggregation_kpi: str,
    groupby_column: str,
    n: int = 10,
    top_or_bottom: Literal['top', 'bottom'] = 'top',
    agg_type: Literal['sum', 'mean', 'median', 'count', 'min', 'max'] = 'sum',
    title: str = None,
    x_label: str = None,
    y_label: str = None,
    palette: str = 'viridis', # or 'coolwarm', 'magma', 'plasma', 'cividis', 'rocket', 'mako'
    figsize: tuple = (14, 7)
):
    """
    Generates a bar plot for a specified aggregation KPI, grouped by a chosen column,
    showing top N or bottom N entries.

    Args:
        df (pd.DataFrame): The input DataFrame.
        aggregation_kpi (str): The name of the KPI column to aggregate (e.g., 'Units', 'Revenue', 'Profit').
        groupby_column (str): The name of the column to group by (e.g., 'Product_Name', 'Store_Name').
        n (int, optional): The number of top or bottom entries to display. Defaults to 10.
        top_or_bottom (Literal['top', 'bottom'], optional): Whether to show 'top' N or 'bottom' N.
                                                            Defaults to 'top'.
        agg_type (Literal['sum', 'mean', 'median', 'count', 'min', 'max'], optional):
                  The type of aggregation to perform. Defaults to 'sum'.
        title (str, optional): Custom title for the plot. If None, a default title is generated.
        x_label (str, optional): Custom label for the x-axis. If None, a default label is generated.
        y_label (str, optional): Custom label for the y-axis. If None, a default label is generated.
        palette (str, optional): Seaborn color palette to use. Defaults to 'viridis'.
        figsize (tuple, optional): Figure size (width, height) in inches. Defaults to (14, 7).
    """

    # --- Input Validation ---
    if aggregation_kpi not in df.columns:
        print(f"Error: Aggregation KPI column '{aggregation_kpi}' not found in DataFrame.")
        return
    if groupby_column not in df.columns:
        print(f"Error: Groupby column '{groupby_column}' not found in DataFrame.")
        return
    if top_or_bottom not in ['top', 'bottom']:
        print("Error: 'top_or_bottom' must be 'top' or 'bottom'.")
        return
    if agg_type not in ['sum', 'mean', 'median', 'count', 'min', 'max']:
        print(f"Error: Invalid 'agg_type'. Must be one of: 'sum', 'mean', 'median', 'count', 'min', 'max'.")
        return

    # --- Perform Aggregation ---
    try:
        aggregated_data = df.groupby(groupby_column)[aggregation_kpi].agg(agg_type)
    except Exception as e:
        print(f"Error during aggregation: {e}")
        print(f"Please ensure '{aggregation_kpi}' is a numeric column and '{agg_type}' is applicable.")
        return

    # --- Sort and Select Top/Bottom N ---
    if top_or_bottom == 'top':
        sorted_data = aggregated_data.sort_values(ascending=False).head(n)
    else: # 'bottom'
        sorted_data = aggregated_data.sort_values(ascending=False).tail(n) # head(n) after ascending sort gives bottom n

    # --- Plotting ---
    sns.set_style("whitegrid") # Apply a clean Seaborn style

    plt.figure(figsize=figsize)

    # Use seaborn.barplot directly with the Series
    # The index of the Series becomes the x-axis, and values become the y-axis
    sns.barplot(x=sorted_data.index, y=sorted_data.values, palette=palette, edgecolor='black')

    # --- Set Titles and Labels ---
    if title is None:
        title = f'{top_or_bottom.title()} {n} {groupby_column.replace("_", " ").title()} by {aggregation_kpi.replace("_", " ").title()}({agg_type.title()})'
    plt.title(title, fontsize=16, fontweight='bold')

    if x_label is None:
        x_label = groupby_column.replace("_", " ").title()
    plt.xlabel(x_label, fontsize=12)

    if y_label is None:
        y_label = f'{agg_type.title()} {aggregation_kpi.replace("_", " ").title()}'
    plt.ylabel(y_label, fontsize=12)

    # Rotate x-axis labels for readability if they are long
    plt.xticks(rotation=90, ha='right', fontsize=10) # 'ha' for horizontal alignment after rotation

    # Customize plot appearance
    plt.grid(False) # Turn off grid as requested in your example
    plt.gca().spines['right'].set_visible(False)
    plt.gca().spines['top'].set_visible(False)
    plt.tick_params(axis='y', labelsize=10)

    plt.tight_layout() # Adjust layout to prevent labels from overlapping
    plt.show()

# --- Example 1: Top 10 Products by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 2: Top 10 Products by count of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='count',
    palette='pastel'
)

# --- Example 3: Top 10 Products by average of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='mean',
    palette='pastel'
)

# --- Example 4: Top 10 Products by max of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='max',
    palette='pastel'
)

# --- Example 5: Top 10 Products by Sum of Revenue ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Product_Name',
    n=10, # Showing top 10 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 6: Top 10 Products by Sum of Profits ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Product_Name',
    n=10, # Showing top 10 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 7: Bottom 10 products by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 8: Bottom 10 products by Sum of revenue  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 9: Bottom 10 products by Sum of profit  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Product_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 1: Top 10 Stores by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 2: Top 10 Stores by Sum of Revenue  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 3: Top 10 Stores by Sum of Profit  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 4: Bottom 10 Stores by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 5: Bottom 10 Stores by Sum of Revenue  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 6: Bottom 10 Stores by Sum of Profit ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Store_Name',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 1: Top 10 City by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 2: Top 10 City by Sum of Revenue  ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 3: Top 10 City by Sum of Profit ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='top',
    agg_type='sum',
    palette='pastel'
)

# --- Example 4: Bottom 10 City by Sum of Units Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Units',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 5: Bottom 10 City by Sum of Revenue Sold ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Revenue',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

# --- Example 6: Bottom 10 City by Sum of Profit ---

plot_aggregated_barplot(
    df=df.copy(),
    aggregation_kpi='Profit',
    groupby_column='Store_City',
    n=10, # Showing top 5 for brevity in output
    top_or_bottom='bottom',
    agg_type='sum',
    palette='pastel'
)

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from typing import List, Literal, Optional

def plot_categorical_composition_stacked_bar(
    df: pd.DataFrame,
    main_category_column: str,
    composition_column: str, # This replaces sub_category_column
    aggregation_kpi: str,     # NEW: KPI to aggregate (e.g., 'Units', 'Revenue', 'Profit')
    agg_type: Literal['sum', 'mean', 'median'] = 'sum', # NEW: Aggregation type for the KPI
    n: Optional[int] = 10,
    top_or_bottom: Literal['top', 'bottom'] = 'top',
    sort_by_total_kpi: bool = True, # NEW: Whether to sort main categories by their total KPI value
    title: str = None,
    x_label: str = 'Proportion (%)',
    y_label: str = None,
    legend_title: str = None,
    colormap: str = 'viridis',
    figsize: tuple = (18, 7)
):
    """
    Generates a 100% stacked bar chart showing the proportional distribution of a
    'composition_column' (e.g., Product_Category) within 'main_category_column' (e.g., Store_Name),
    based on the aggregation of a specified KPI (e.g., Units, Revenue, Profit).

    Args:
        df (pd.DataFrame): The input DataFrame.
        main_category_column (str): The column that will form the main bars (e.g., 'Store_Name').
        composition_column (str): The column whose values will form the segments within each bar
                                  (e.g., 'Product_Category', 'Store_Location').
        aggregation_kpi (str): The name of the KPI column to aggregate (e.g., 'Units', 'Revenue', 'Profit').
        agg_type (Literal['sum', 'mean', 'median']): The aggregation type for the KPI. Defaults to 'sum'.
        n (Optional[int], optional): The number of top/bottom `main_category_column` entries to display.
                                     If None, all unique entries are displayed. Defaults to 10.
        top_or_bottom (Literal['top', 'bottom'], optional): Whether to select 'top' N or 'bottom' N
                                                             `main_category_column` entries.
                                                             Only relevant if `n` is not None. Defaults to 'top'.
        sort_by_total_kpi (bool, optional): If True, main categories are sorted by their total KPI value
                                            before selecting top/bottom N. If False, they are sorted
                                            alphabetically by `main_category_column`. Defaults to True.
        title (str, optional): Custom title for the plot. If None, a default title is generated.
        x_label (str, optional): Custom label for the x-axis. Defaults to 'Proportion (%)'.
        y_label (str, optional): Custom label for the y-axis. If None, a default label is generated.
        legend_title (str, optional): Title for the legend. If None, `composition_column` is used.
        colormap (str, optional): Matplotlib colormap to use for the stacked bars. Defaults to 'viridis'.
        figsize (tuple, optional): Figure size (width, height) in inches. Defaults to (16, 8).
    """

    # --- Input Validation ---
    if main_category_column not in df.columns:
        print(f"Error: Main category column '{main_category_column}' not found in DataFrame.")
        return
    if composition_column not in df.columns:
        print(f"Error: Composition column '{composition_column}' not found in DataFrame.")
        return
    if aggregation_kpi not in df.columns:
        print(f"Error: Aggregation KPI '{aggregation_kpi}' not found in DataFrame.")
        return
    if top_or_bottom not in ['top', 'bottom']:
        print("Error: 'top_or_bottom' must be 'top' or 'bottom'.")
        return
    if agg_type not in ['sum', 'mean', 'median']:
        print(f"Error: Invalid 'agg_type'. Must be one of: 'sum', 'mean', 'median'.")
        return
    if not pd.api.types.is_numeric_dtype(df[aggregation_kpi]):
        print(f"Error: Aggregation KPI '{aggregation_kpi}' must be a numeric column.")
        return

    # --- Group and Aggregate the KPI ---
    # Group by both main and sub categories and aggregate the KPI
    grouped_data = df.groupby([main_category_column, composition_column])[aggregation_kpi].agg(agg_type)

    # Unstack the composition_column to get the format needed for stacked bar
    # This creates a table where rows are main_categories and columns are composition_categories
    # and values are the aggregated KPI.
    pivot_table_kpi = grouped_data.unstack(fill_value=0) # fill_value=0 for combinations not present

    # --- Select Top/Bottom N Main Categories based on KPI total ---
    if n is not None:
        if sort_by_total_kpi:
            # Sort by the total (or mean, median) of the KPI for each main category
            main_category_kpi_totals = pivot_table_kpi.sum(axis=1) # Sum across all composition categories for sorting
            if top_or_bottom == 'top':
                selected_main_categories = main_category_kpi_totals.nlargest(n).index
            else: # 'bottom'
                selected_main_categories = main_category_kpi_totals.nsmallest(n).index
            # Ensure the selected categories are present in the pivot table index
            pivot_table_kpi = pivot_table_kpi.loc[pivot_table_kpi.index.isin(selected_main_categories)]
        else:
            # Sort alphabetically and then select top/bottom N
            sorted_indices = sorted(pivot_table_kpi.index)
            if top_or_bottom == 'top':
                selected_main_categories = sorted_indices[:n]
            else:
                selected_main_categories = sorted_indices[-n:]
            pivot_table_kpi = pivot_table_kpi.loc[selected_main_categories]

    # Ensure the order is based on total KPI value if sorted
    if sort_by_total_kpi:
        pivot_table_kpi = pivot_table_kpi.loc[main_category_kpi_totals.reindex(pivot_table_kpi.index).sort_values(ascending=False if top_or_bottom == 'top' else True).index]


    # --- Convert Aggregated KPI Values to Proportions (Percentages) ---
    # Divide each row's KPI values by its total KPI sum to get proportions
    # Multiply by 100 to get actual percentage values for plotting
    percentage_pivot_table = pivot_table_kpi.apply(lambda r: r / r.sum(), axis=1) * 100

    # Handle cases where a main_category might have 0 total KPI (e.g., if filtered to nothing)
    # This prevents NaN in percentage calculation if r.sum() is 0.
    percentage_pivot_table.replace([np.inf, -np.inf], np.nan, inplace=True)
    percentage_pivot_table.fillna(0, inplace=True)


    # --- Plotting ---
    sns.set_style("whitegrid")
    plt.figure(figsize=figsize)

    ax = percentage_pivot_table.plot(
        kind='barh',
        stacked=True,
        cmap=colormap,
        ax=plt.gca(),
        edgecolor='black',
        width=0.8
    )

    # --- Add Percentage Labels to Bars ---
    for container in ax.containers:
        # Filter for values > 0 to avoid cluttering with 0% labels
        labels = [f'{w:.1f}%' if w > 0.0 else '' for w in container.datavalues]
        ax.bar_label(container, labels=labels, label_type='center', fontsize=9, color='black', fontweight='bold')

    # --- Set Titles and Labels ---
    if title is None:
        title_prefix = f'{top_or_bottom.title()} {n}' if n else 'Overall'
        title = (f'{title_prefix} {main_category_column.replace("_", " ").title()} '
                 f'Composition by {composition_column.replace("_", " ").title()} '
                 f'({agg_type.title()} {aggregation_kpi.replace("_", " ").title()})')
    plt.title(title, fontsize=16, fontweight='bold')

    plt.xlabel(x_label, fontsize=12)

    if y_label is None:
        y_label = main_category_column.replace("_", " ").title()
    plt.ylabel(y_label, fontsize=12)

    # --- Customize Plot Appearance ---
    plt.xticks(fontsize=10)
    plt.yticks(fontsize=10)
    plt.xlim(0, 100) # X-axis must be 0 to 100 for percentage
    plt.grid(False)
    plt.gca().spines['right'].set_visible(False)
    plt.gca().spines['top'].set_visible(False)
    plt.gca().spines['left'].set_visible(False) # Hide left spine for cleaner look

    # Add legend
    if legend_title is None:
        legend_title = composition_column.replace("_", " ").title()
    plt.legend(title=legend_title, bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)

    plt.tight_layout(rect=[0, 0, 0.85, 1])
    plt.show()

# 1. Top 10 Stores by Total Unit sold, showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_Name',
    composition_column='Product_Category',
    aggregation_kpi='Units',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 Stores: Unit Sales Distribution by Product Category',
    colormap='Set2'
)

# 2. Top 10 Stores by Total Revenue, showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_Name',
    composition_column='Product_Category',
    aggregation_kpi='Revenue',
    agg_type='sum',
    n=10,
    top_or_bottom='bottom',
    sort_by_total_kpi=True,
    title='Top 10 Stores: Revenue Distribution by Product Category',
    colormap='Set2'
)

# 3. Top 10 Stores by Total Profit, showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_Name',
    composition_column='Product_Category',
    aggregation_kpi='Profit',
    agg_type='sum',
    n=10,
    top_or_bottom='bottom',
    sort_by_total_kpi=True,
    title='Top 10 Stores: Profit Distribution by Product Category',
    colormap='Set2'
)

# 1. Top 10 City by Total Units Sold, showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_City',
    composition_column='Product_Category',
    aggregation_kpi='Units',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 City : Unit Sold Distribution by Product Category',
    colormap='Set2'
)

# 2. Top 10 City by Total Revenue , showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_City',
    composition_column='Product_Category',
    aggregation_kpi='Revenue',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 City : Revenue Distribution by Product Category',
    colormap='Set2'
)

# 3. Top 10 City by Total Profit , showing composition by Product Category


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Store_City',
    composition_column='Product_Category',
    aggregation_kpi='Profit',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 City : Profit Distribution by Product Category',
    colormap='Set2'
)

# 1. Top 10 Products by Total Units Sold, showing composition by Store Location


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Product_Name',
    composition_column='Store_Location',
    aggregation_kpi='Units',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 Products : Unit Sold Distribution by Store Location',
    colormap='Set2'
)

# 2. Top 10 Products by Total Revenue , showing composition by Store Location


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Product_Name',
    composition_column='Store_Location',
    aggregation_kpi='Revenue',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 Products : Revenue  Distribution by Store Location',
    colormap='Set2'
)

# 3. Top 10 Products by Total Profit , showing composition by Store Location


plot_categorical_composition_stacked_bar(
    df=df.copy(),
    main_category_column='Product_Name',
    composition_column='Store_Location',
    aggregation_kpi='Profit',
    agg_type='sum',
    n=10,
    top_or_bottom='top',
    sort_by_total_kpi=True,
    title='Top 10 Products : Profit  Distribution by Store Location',
    colormap='Set2'
)

	Date
0	1/1/2022
1	1/2/2022
2	1/3/2022
3	1/4/2022
4	1/5/2022
...	...
633	9/26/2023
634	9/27/2023
635	9/28/2023
636	9/29/2023
637	9/30/2023

	Table	Field	Description
0	Products	Product_ID	Product ID
1	Products	Product_Name	Product name
2	Products	Product_Category	Product Category
3	Products	Product_Cost	Product cost ($USD)
4	Products	Product_Price	Product retail price ($USD)
5	Inventory	Store_ID	Store ID
6	Inventory	Product_ID	Product ID
7	Inventory	Stock_On_Hand	Stock quantity of the product in the store (in...
8	Stores	Store_ID	Store ID
9	Stores	Store_Name	Store name
10	Stores	Store_City	City in Mexico where the store is located
11	Stores	Store_Location	Location in the city where the store is located
12	Stores	Store_Open_Date	Date when the store was opened
13	Sales	Sale_ID	Sale ID
14	Sales	Date	Date of the transaction
15	Sales	Store_ID	Store ID
16	Sales	Product_ID	Product ID
17	Sales	Units	Units sold
18	Calendar	Date	Calendar date

	Sale_ID	Date	Store_ID	Product_ID	Units
0	1	2022-01-01	24	4	1
1	2	2022-01-01	28	1	1
2	3	2022-01-01	6	8	1
3	4	2022-01-01	48	7	1
4	5	2022-01-01	44	18	1

	Product_ID	Product_Name	Product_Category	Product_Cost	Product_Price
0	1	Action Figure	Toys	$9.99	$15.99
1	2	Animal Figures	Toys	$9.99	$12.99
2	3	Barrel O' Slime	Art & Crafts	$1.99	$3.99
3	4	Chutes & Ladders	Games	$9.99	$12.99
4	5	Classic Dominoes	Games	$7.99	$9.99

	Store_ID	Store_Name	Store_City	Store_Location	Store_Open_Date
0	1	Maven Toys Guadalajara 1	Guadalajara	Residential	1992-09-18
1	2	Maven Toys Monterrey 1	Monterrey	Residential	1995-04-27
2	3	Maven Toys Guadalajara 2	Guadalajara	Commercial	1999-12-27
3	4	Maven Toys Saltillo 1	Saltillo	Downtown	2000-01-01
4	5	Maven Toys La Paz 1	La Paz	Downtown	2001-05-31

	count
Product_Price
15.99	113993
14.99	103387
19.99	92276
6.99	68083
2.99	64834
10.99	55500
3.99	54078
39.99	48030
9.99	42698
12.99	41931
8.99	41559
20.99	26748
5.99	20776
4.99	20736
25.99	10494
24.99	8552
11.99	6812
7.99	2543

	count
Product_Cost
1.99	118912
3.99	114950
9.99	112575
6.99	97366
13.99	63879
34.99	48030
7.99	45705
10.99	41550
14.99	39252
17.99	38703
11.99	32291
5.99	24507
20.99	16561
8.99	14575
4.99	7804
2.99	6370

Data Analysis¶

Notebook Structure¶

1. Import necessary libraries¶

2. Import all the necessary datasets¶

3. Visualizing the dataset¶

4. Merging the dataset¶

A. Merging sales and products on 'Product_ID'¶

B. Merging sales_products with stores on 'store_ID'¶

C. Merging sales_products_stores with inventory on the composite key ['Product_ID' , 'Store_ID']¶

D. Storing it in a new dataframe¶

5. Dataset summarization¶

6. Important Pre-computation/Cleaning¶

A. conversion of Date and Store_Open_Date to datetime¶

B. Product_Cost and Product_Price must be converted to numerical (float/int) after handling any non-numeric characters¶

C. Derive new metrics¶

7. Exploratory Data Analysis - Univariate Analysis of Numerical Variables¶

A. Quantitative Analysis¶

B. Visualization of data distribution using Histograms with KDE¶

C. Visualizing the discrete numerical variables¶

8. Exploratory Data Analysis - Univariate Analysis of Categorical Variables¶

A. Quantitative analysis¶

B. Visualization using Bar Plots (Count Plots)¶

C. Visualization using Donut chart¶

9. Exploratory Data Analysis - Timeseries Analysis - Absolute Values of the KPI's¶

A. What is the date range of the dataset?¶

B. Utility function to aggregate KPI's ( Units , Revenue and Profit ) at different frequency ( Weekly , Monthly and Quarterly )¶

C. Execute the aggregation function - Weekly aggregation of KPI's ( Units , Revenue and Profit )¶

D. Execute the aggregation function - Monthly aggregation of KPI's ( Units , Revenue and Profit )¶

E. Execute the aggregation function - Quarterly aggregation of KPI's ( Units , Revenue and Profit )¶

F. Utility function for data visualization of timeseries data at different frequency¶

G. Timeseries visualization of Weekly aggregated KPI's¶

Interpretation¶

H. Timeseries visualization of Monthly aggregated KPI's¶

Interpretation¶

I. Timeseries visualization of Quarterly aggregated KPI's¶

Interpretation¶

10. Exploratory Data Analysis - Timeseries Analysis - Growth of the KPI's¶

A. Define the utility function to calculate the growth at different frequency ( wow , mom and qoq )¶

B. Execute the growth function - Weekly growth of KPI's ( Units , Revenue and Profit )¶

C. Execute the growth function - Monthly growth of KPI's ( Units , Revenue and Profit )¶

D. Execute the growth function - Quarterly growth of KPI's ( Units , Revenue and Profit )¶

E. Timeseries visualization of Quarterly growth KPI's¶

Interpretation¶

F. Timeseries visualization of Monthly growth KPI's¶

Interpretation¶

G. Timeseries visualization of Weekly growth KPI's¶

Interpretation¶

11. Bivariate analysis - Numerical Vs Categorical variable¶

A. Top 10 and Bottom 10 Products based on sum of different KPI's ( Units , Revenue , Profit)¶

B. Aggregated ordering of Product category on sum of different KPI's ( Units , Revenue , Profit )¶

C. Top 10 and Bottom 10 Stores based on sum of different KPI's ( Units , Revenue , Profit)¶

D. Top 10 and Bottom 10 City based on sum of different KPI's ( Units , Revenue , Profit)¶

E. Aggregated ordering of Store Location on sum of different KPI's ( Units , Revenue , Profit )¶

F. Utility function for Top N and Bottom N plotting post aggregation¶

G. Data visualization for Top N and Bottom N Products based on aggregation of different KPI's ( Units , Revenue , Profit )¶

Interpretation :¶

Interpretation¶

H. Data Visualization for Top 10 and Bottom 10 Stores based on sum of different KPI's ( Units , Revenue , Profit)¶

Interpretation¶

Interpretation¶

I. Data visualization for Top 10 and Bottom 10 City based on sum of different KPI's ( Units , Revenue , Profit)¶

Interpretation¶

Interpretation¶

12. Multivariate analysis¶

A. Utility function to create the composition stacked bar¶

B. Top 10 store aggregated by KPI's ( Units , Revenue and Product ) analysis by product category composition¶

Interpretation¶

C. Top 10 city aggregated by KPI's ( Units , Revenue and Product ) analysis by product category composition¶

Interpretation¶

D. Top 10 products aggregated by KPI's ( Units , Revenue and Product ) analysis by store location composition¶

Interpretation¶

Final Recommendation¶

COMPLETED¶